Cancer of the brain and other nervous systems is no doubt rare—given the low risk of developing the cancer. However, the cancer types have been increasingly difficult to diagnose, consequently making prognosis quite hard. Now contrast with the low survival rate of patients who have the cancer but are not diagnosed—not even 33% for five-year rate. This puts a huge burden on primary care on the primary and secondary care. Stridently, the unmet need then is to reduce the time-to-diagnosis to aid prognosis. Most importantly, the patient’s pathway suffers greatly from the lack of a cost-effective and non-invasive diagnosis methodology. Recently a team of researchers from the University of Strathclyde, claimed to come out with a triage test for detecting brain cancer.
Triage Tool to Improve Patient’s Pathway due to High Health Care Cost Savings
The novel methodology is based on total reflection (ATR)-Fourier transform infrared (FTIR) spectroscopy and integrates machine learning algorithm. The researchers tested the simple yet cost-effective method in a cohort of 724 people including those that didn’t have cancer, i.e., control participants without the disease. To this end, the team used the methodology to understand the biochemical profile of a blood, without an extensive set-up. The results are encouraging—a sensitivity and specificity of 83.3% and 87.0% in cancer and control patients respectively; adjustments are made for retrospective results.
The innovative method is a potential candidate for clinical translation, contend the researchers. Despite the limitations, they consider this novel since the signs of brain cancer in individuals are quite simple. Sometimes, the tumor may lie hidden in simple headache episodes in primary care consultations. All this makes triaging of patients a hard nut to crack. Hence, clinicians must be able to differentiate between people so that they get access to urgent imaging of the tumor. The results are also breather for patients who may be suffering from aggressive cancer, notably glioblastoma.